Some embodiments described herein relate generally to detecting non-intended network traffic using network visitation information.
Network service providers such as, for example, advertisers or online markets use streams of network data to understand user behavior, relying on the fact that the observed actions represent the intentions of real network users. The service providers typically reply on understanding users' intentions to determine when and to whom to provide a service (e.g., an advertisement). Some service providers, however, use approaches for inflating traffic that does not coincide with real users' intentions, for example, by automatically redirecting a user to a network location after the user selects a different network location (e.g., a website), or by loading a website in the background while the user is viewing other content. This can artificially increase the amount of traffic for certain network locations by increasing the number of non-intended visits by users, thereby allowing these network locations to charge more for certain services such as advertisements.
Known methods have been developed to explicitly observe mechanisms that produce non-intended user visits to network locations and identify network locations with non-intended traffic. These known methods, however, are inadequate because the mechanisms that a network location uses for producing non-intended traffic have to be individually identified for each network location.
Therefore, a need exists to overcome the shortcomings of the known methods by detecting non-intended traffic using co-visitation information.